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Species Diversity

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Title: Species Diversity


1
Species Diversity
  • Chapter 16

2
Introduction
  • 1710, Dutch colonists on the Indonesian island of
    Bangka.
  • Over the centuries, mining and deforestation on a
    massive scale.
  • Slash and burn agriculture.
  • 3 of the primary forest remained intact.

3
Introduction
  • Minimal restoration efforts and bird diversity.
  • First glance restored areas looked the same as
    unrestored sites.

4
Introduction
  • Further analysis restored sites had more
    diversity.

5
Measures of Diversity
  • Simplest measure count number of species in an
    area (species richness).
  • Major problem does not take into account species
    abundance.
  • Community A has 99 individuals of species 1 and 1
    individual of species 2.
  • Community B has 50 individuals of species 1 and
    50 individual of species 2.
  • Both communities have the same species richness.

6
Diversity Indices
  • Species richness is highly susceptible to sample
    size.
  • The greater the number of individuals in the
    sample, the higher the number of species
    recorded.

7
Diversity Indices
  • Therefore, we need diversity indices that
    incorporate both abundance and richness.

8
Diversity Indices
  • Two broad categories dominance indices and
    information-statistic indices.

9
Diversity Indices
  • Dominance indices
  • These indices are weighted toward the abundance
    of the commonest species.
  • Berger and Parker dominance index (1970).

10
Dominance Indices
  • Concept if one species dominates a community,
    then the community is not very diverse.
  • Community 1 D 10/100 0.1
  • DBP 1/0.1 10
  • Community 2 D 55/100 0.55
  • DBP 1/0.55 1.82

11
Dominance Indices
  • Simpsons index (1949)
  • Gives the probability that any two individuals
    drawn at random from an infinitely large
    community will belong to different species.
  • Disadvantage it is heavily weighted toward the
    most abundant species.
  • Common to all dominance indices.
  • Rare species fail to change index.
  • Regarded as more accurate than Berger-Parker
    Index.

12
Diversity Indices
  • Information-statistic indices
  • Can take into account rare species.
  • Based on the rationale that diversity in a
    natural system can be measured in a way that is
    similar to the way information contained in a
    code or message is measured.
  • A technique to measure uncertainty.
  • The higher the value, the higher the uncertainty
    in predicting a sequence.
  • The more diverse a community.

13
Information-Statistic Indices
  • Shannon index
  • Even the rare species contributes to the Shannon
    index, so if an area has many rare species, their
    contributions would accumulate.
  • Shannon index for real communities usually fall
    between 1.5 and 3.5.

14
Information-Statistic Indices
  • Brillouin index
  • Index is designed to reflect species abundance.

15
Information-Statistic Indices
  • Differences between Brillouin index and Shannon
    index
  • Shannon index does not change with abundance, as
    long as the proportional abundance remains
    constant. Brillouin index does change.

16
Information-Statistic Indices
  • Brillouin index uses factorials, which quickly
    produces huge numbers that are unwieldy. Shannon
    index is often chosen for its computational
    simplicity.

17
Diversity Indices
  • Comparison of Berger-Parker, Simpson, Shannon,
    and Brillouin.
  • Generally values of diversity are correlated
    increasing together as diversity increases.

18
Diversity Indices
  • Example
  • Indices generally give the same results.
  • 3 out of 4 indicate that bird diversity is
    greater in the restored site.
  • If rare species are valued, then
    information-statistic are the most valuable.
  • If total abundance of individuals is important,
    then Brillouin is the best index.

19
Diversity Indices
  • Summary of effectiveness

20
Diversity Indices
  • It is important to remember that we cant mix the
    indices when comparing communities the SAME
    index must be used throughout.

21
Diversity Indices
  • Evenness
  • Information-statistic indices are affected by
    both number of species and their equitability or
    evenness.
  • Compare a communitys actual diversity, HS, to
    the maximum possible diversity, Hmax.
  • Evenness H / Hmax
  • E is constrained between 0 and 1.0.
  • Generally, adding species and increasing evenness
    both increase species diversity.

22
Weighting Biodiversity Indices
  • Weighting biodiversity indices the use of
    ordinal indices.
  • Most indices discussed so far, treat species
    equally (cardinal indices).
  • Is this right, or should weight be used?
  • Should more weight be given to a large, rare
    predator or a small, rare nematode?

23
Applied Ecology
  • Ordinal indices indices that attempt to rank
    order species in importance.
  • Cardinal indices indices that treat all species
    as equals.

24
Applied Ecology
  • Vane-Wright, Humphries and Williams (1991).
  • Developed methods to weight taxonomically rare
    species in diversity indices.

25
Rank Abundance Diagrams
  • A more complete picture of the distribution of
    species abundance.
  • Plotting proportional abundance (usually on a
    logarithmic scale) against rank abundance.

26
Rank Abundance Diagrams
  • Rank abundance diagrams can be drawn for the
    number of individuals, biomass, ground area
    covered (and other variables) plotted against
    rank abundance.
  • At least 18 different theoretical forms.
  • Three most well-known
  • Geometric series
  • Lognormal
  • Broken-stick

27
Rank Abundance Diagrams
  • Species abundance is most equitable in the
    broken-stick, less equitable in the lognormal,
    and less equitable still in the geometric series.

28
Geometric Series
  • The first or most dominant species to colonize a
    new area appropriates a fraction of the available
    resources, and by competitive interaction,
    preempts that fraction.
  • Second species then preempts a similar fraction,
    and so on.

29
Geometric Series
  • Model fits
  • Plants from sub-alpine communities.
  • Benthos in a polluted fjord.
  • Best fit for communities of relatively few
    species, where a single environmental factor
    predominates, such as extreme temperature or
    pollution.

30
Broken Stick
  • Resources (represented by a stick) are divided at
    one time into segments over the length of the
    stick.
  • Implying instantaneous colonization by all
    species.
  • The segments are ranked in decreasing order of
    their lengths.
  • Abundance of species is assumed to be
    proportional to the length of the segment.

31
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32
Broken Stick
  • Model fits
  • A few bird, fish, worm, and predatory gastropod
    communities.
  • Realistic on relatively few occasions.

33
Lognormal
  • Developed by Frank Preston (1948).
  • More common than broken stick for most
    communities that are rich in species.
  • Graph of species number versus a logarithmic
    scale.

34
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35
Lognormal
  • Lognormal distribution of a variety of
    communities.

36
Lognormal
  • Existence of the truncated lognormal
    distribution.
  • Plots of number of species (y-axis) against log
    of individuals per species (x-axis), were
    truncated to the left of the mode.
  • Truncation due to species that were present in
    the habitat but not in the sample.

37
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38
Lognormal
  • If larger samples were taken, more species would
    be obtained.
  • Mode would move to the right.
  • Therefore, it is critical that communities are
    adequately sampled.

39
Community Similarity
  • Similarity coefficients a method for directly
    comparing diversity of different sites.
  • Usually compare the number of species common to
    all areas.

40
Community Similarity
  • Comparison will involve a simple presence-absence
    matrix for two areas, A and B.
  • a number of species common to both sites.
  • b number of species in site B, but not in A.
  • c number of species in site A, but not in B.
  • d number of species absent in both samples.

41
Community Similarity
  • Is "d", meaningful?
  • d is a measure of the negative matches,
    potentially biologically meaningful.
  • In reality, it is almost impossible to know.
  • Most similarity coefficients rely only on a, b,
    and c.

42
Community Similarity
  • Community similarity can be measured using
    similarity coefficients such as
  • Jaccard coefficient
  • Sorenson coefficient
  • Simple matching coefficient (incorporates d)

43
Cluster Analysis
  • Cluster Analysis is used when research is being
    conducted on more than two sites.
  • Starts with a table or matrix giving the
    similarity between each pair of sites (by using
    any similarity coefficient).

44
Cluster Analysis
  • The two most similar sites are combined to form a
    single cluster.
  • The analysis then proceeds by successfully
    combining similar sites until all are combined
    into a single figure (dendrogram).

45
Cluster Analysis
  • Single-linkage clustering method for combining
    sites into clusters.
  • Begin with a matrix of similarity coefficients.
  • Find most similar pairs (Conifer sites 2 and 3).

46
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47
Cluster Analysis
  • Second most similar sites (Oak sites 1 and 2).
  • Third most similar sites (Conifer sites 1 and 2).
  • Therefore, conifer site 1 is linked with cluster
    of conifer site 2 and 3, forming a 3 community
    cluster.
  • Last joining is between oak 1 and 2 cluster, and
    conifer 1, 2, and 3 cluster.

48
Conclusion
  • Measuring biodiversity is essential for effective
    management practices.
  • Another important reason to measure biodiversity
  • Diversity begets stability!
  • Stable ecosystems are easier to manage.

49
Summary
  • Biodiversity may be expressed in a number of
    ways species richness or by various indices that
    take into account richness and abundance.

50
Summary
  • Dominance indices include Simpsons index and the
    Berger-Parker index. Both are easy to calculate
    and interpret, but both are biased toward
    dominant or common species. Simpsons index may
    be more realistic.

51
Summary
  • Information-statistic indices pay more attention
    to rare species. Shannon index and Brillouin
    index are information-statistic indices. The
    Brillouin index is more sensitive to species
    abundance, but is much more difficult to
    calculate.

52
Summary
  • Each of the various dominance and
    information-statistic indices may give values
    slightly different than those obtained from other
    indices, but all of the values are generally well
    correlated. Each has its own strength.
    Choosing the appropriate index for the
    appropriate situation depends on the question
    being asked.

53
Summary
  • Most diversity indices can be referred to as
    cardinal indices (they treat species equally).
    Indices that attempt to weight rare species or
    any other type of species are known as ordinal
    indices.

54
Summary
  • Diversity indices attempt to describe whole
    communities with one statistic. A more complete
    description can be obtained by plotting
    proportional abundance of every species against
    the rank of its abundance. Examples of
    abundance diagrams geometric, lognormal, and
    broken stick.

55
Summary
  • To compare diversity among areas, compare
    diversity indices or use similarity coefficients.

56
Summary
  • Cluster analysis may be used to compare the
    similarity of two or more sites.
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